Transportation organizations have to take a range of variables into account when analyzing data including population estimates, holiday celebrations, fluctuating gas prices and even weather reports. The mass of data you have to sift through makes it difficult to conduct data analysis in a systematic and logical way. Most predictive models used for planning and scheduling transportation projects are based on datasets that have been analyzed in a relatively ad hoc manner.
Heinrich Group’s state of the art machine learning software can produce fast, accurate, and scalable predictive models to help you:
Improve operational efficiency
- Reduce human error. Heinrich Group’s machine learning combines the power, speed and accuracy of high performance advanced analytics to process massive data sets
- Boost issue detection. Analyze traffic patterns and spot potential issues in traffic control early on to implement preventive measures
Generate new insights
- Think outside the box. Heinrich Group’s machine learning algorithms can find new insights and make new discoveries that may otherwise go unnoticed. Machine learning can be used to analyze commuter attributes and traffic triggers to predict which area during specific commuting hours are most likely to be congested.
- Get more and more. Heinrich Group’s software has the ability to accept structured and unstructured data from different sources. Add more data sources to generate even more accurate predictive models.